NONLINEAR ESTIMATION OF AGGREGATE PRODUCTION FUNCTIONS.
The Cobb-Douglas form for the economy-wide production function has been popular, in both theoretical and empirical analyses. Production functions and associated marginal productivity relations are essentially non-linear relationships, but this aspect has been disguised or circumvented by transformat...
| Publicado en: | Review of Economics & Statistics Vol. 49; no. 1; pp. 28 - 45 |
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| Autores principales: | , |
| Formato: | Artículo |
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MIT Press
Feb67
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=4645344&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 4645344 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346535 RMS jtl: Review of Economics & Statistics issn: 00346535 maglogo: N pubinfo: dt: Feb67 vid: 49 iid: 1 pid: 776 pub: MIT Press artinfo: ui: 4645344 10.2307/1937881 ppf: 28 ppct: 17 formats: tig: atl: NONLINEAR ESTIMATION OF AGGREGATE PRODUCTION FUNCTIONS. aug: au: Bodkin, Ronald G. Klein, Lawrence R. su: Production functions (Economic theory) Estimation theory Nonlinear statistical models Marginal productivity Nonlinear theories Computer software Production (Economic theory) Economic models Mathematical models of consumption sug: subj: Production functions (Economic theory) Estimation theory Nonlinear statistical models Marginal productivity Nonlinear theories Computer software Production (Economic theory) Economic models Mathematical models of consumption ab: The Cobb-Douglas form for the economy-wide production function has been popular, in both theoretical and empirical analyses. Production functions and associated marginal productivity relations are essentially non-linear relationships, but this aspect has been disguised or circumvented by transformation of variables and stepwise procedures of estimation. With the help of new computer programs being developed, it intend to cut through these nonlinearities to obtain direct estimates of the parameters, through the use of nonlinear methods. Maximum likelihood estimation of parameters in a system of equations follows the same principles. Even if the equations to be estimated are linear, the joint likelihood function will generally lead to nonlinear estimation equations. Hence, essentially the same methods that have been used for solving the nonlinear estimation equations of maximum likelihood estimation for a system of linear structural equations can be employed in the treatment of a nonlinear structural system. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 1967 holdings: @attributes: islocal: N |
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